Senior Staff Software Engineer, Machine Learning Frameworks
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Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience in software development.
- 8 years of experience designing, building, and operating high-leverage software systems, with 5 years dedicated to large-scale, distributed machine learning infrastructure (training or serving).
- 7 years of experience leading technical project strategy, ML design, and working with ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
- 5 years of experience with design and architecture; and testing/launching software products.
- 5 years of experience in Machine Learning, Distributed Computing and AI Algorithms
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- 8 years of experience with data structures/algorithms.
- 5 years of experience in a technical leadership role leading project teams and setting technical direction.
- Experience in any one of the modern ML framework architectures (JAX, TensorFlow, PyTorch) with the knowledge of ML compiler toolchains (e.g., XLA).
- Experience with performance I/O, data pipeline optimization, or low-latency runtime systems (e.g., experience with distributed parameter server training or high-throughput serving runtimes).
About the job
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.We are a core initiative of Core ML Frameworks, dedicated to building the world's leading Artificial Intelligence/Machine Learning (AI/ML) ecosystem for Google’s product teams. Our mission is to accelerate Google's strategic transition to JAX, crafting the foundational infrastructure that powers the next generation of AI. We focus on driving performance, scalability, and modularity, ensuring Google maintains its global leadership by providing future-proof machine learning frameworks and infrastructure. Join us to define the architecture and build the systems that serve users daily.The ML, Systems, & Cloud AI (MSCA) organization at Google designs, implements, and manages the hardware, software, machine learning, and systems infrastructure for all Google services (Search, YouTube, etc.) and Google Cloud. Our end users are Googlers, Cloud customers and the billions of people who use Google services around the world.
We prioritize security, efficiency, and reliability across everything we do - from developing our latest TPUs to running a global network, while driving towards shaping the future of hyperscale computing. Our global impact spans software and hardware, including Google Cloud’s Vertex AI, the leading AI platform for bringing Gemini models to enterprise customers.
Responsibilities
- Own the technical goal and architectural strategy for enabling Google’s production workloads to migrate to the native JAX ecosystem, driving adoption across core business verticals (e.g., Ads, Search, YouTube).
- Identify, analyze, and systematically eliminate systemic performance bottlenecks to ensure JAX delivers efficiency across Google's hardware fleet.
- Define the comprehensive standards for production-grade JAX workflows, spanning data input, highly distributed training loops, efficient model checkpointing, and high-throughput serving, eliminating dependency on legacy TensorFlow components.
- Act as a key technical leader interfacing with Google-wide stakeholders (XLA/Compilers, Platform Teams, and major ML consumers) to drive alignment and ensure the successful delivery of a cohesive, future-proof ML infrastructure roadmap.
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